Miguel Rocha is currently an Associate Professor at the Informatics Department (http://www.di.uminho.pt) in the School of Engineering, University of Minho, Portugal.
He is also a researcher within the Centre of Biological Engineering (CEB) (http://ceb.uminho.pt), where he co-leads a research team in Bioinformatics and Systems Biology, integrated within the newly formed Biosystems group (http://ceb.uminho.pt/biosystems/Labs?lab=1), that currently involves over 25 researchers. He is the author of around 120 publications in international journals and in peer-reviewed conferences, from which around 90 are indexed in ISI Web of Science. Also, over the last few years he has been the PI and has collaborated in several funded research projects by the Portuguese FCT, European Commission and private companies.
He currently teaches courses at the undergraduate, master and doctoral levels in the areas of Bioinformatics, Machine Learning/ Data Mining and basic Computer Science. He is on the board of the master course in Bioinformatics, a degree that he co-founded in 2007 and from which he was the first Director (2007 to 2010). He was also the Director of the Computer Sciences and Technologies Centre (CCTC) from 2010 to 2013.
Furthermore, he is one of the founders and the Chief Technological Officer of the spin-off company Silico Life (http://www.silicolife.com), created in 2010, that offers Bioinformatics and Computational Biology solutions for the industry. The company has won a national prize in entrepreneurship (Atreve-te 2010 contest).
Miguel Rocha graduated in Systems and Informatics Engineering (1995) from the University of Minho, the institution where he also did the Master in Informatics (1998) and the PhD in Informatics (2004).
Computational tools for metabolomics data mining: pplications in natural products and food research
Metabolomics data plays an important role in the functional analysis of biological systems, allowing a direct focus on the metabolism of microbes, plants and other complex organisms by addressing the measurement of the amounts of metabolites in biological samples. In this talk, we will discuss some of the computational tools developed in our work for the analysis of this type of data, encompassing data coming from measurement technologies such as gas or liquid chromatography coupled with mass spectrometry, nuclear magnetic resonance and other types of spectral data, as infra-red, ultra-violet visible or Raman. The R specmine package will be highlighted, as well as the web-based interfaces for its improved utilization.
We will also address a number of applications of metabolomics data analysis and mining, related to the analysis of natural products (e.g. propolis) or food research, both in agriculture (e.g. cassava, maize or rice) and aquiculture.